Adversarial Machine Learning
Scope
Use this skill when working on:
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Adversarial examples (perturbations that fool models)
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Data poisoning attacks
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Model backdoors and trojans
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Evasion attacks
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Membership inference and model inversion
Attack Taxonomy
Adversarial Examples
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White-box attacks (full model access)
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Black-box attacks (query-only access)
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Transferability attacks
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Physical-world adversarial examples
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Patch attacks
Poisoning Attacks
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Label flipping
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Clean-label poisoning
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Gradient-matching poisoning
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Backdoor insertion during training
Backdoor Attacks
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Trojan triggers (visual patterns, specific inputs)
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Instruction backdoors (for LLMs)
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Weight-space backdoors
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Supply chain backdoors
Evasion Attacks
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Feature-space evasion
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Problem-space evasion
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Adaptive attacks against defenses
Privacy Attacks
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Membership inference attacks (MIA)
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Model inversion attacks
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Training data extraction
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Model stealing/extraction
Defense Categories
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Adversarial training
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Certified robustness
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Input preprocessing
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Anomaly detection
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Differential privacy
Key Frameworks & Tools
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Adversarial Robustness Toolbox (ART) - IBM
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CleverHans - TensorFlow
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Foolbox - PyTorch/JAX/TensorFlow
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TextAttack - NLP adversarial attacks
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SecML - Secure ML library
Where to Add Links in README
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Adversarial example tools: AI Security & Attacks → Adversarial Attacks
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Poisoning/backdoor research: AI Security & Attacks → Poisoning & Backdoors
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Privacy attacks: AI Security & Attacks → Privacy & Extraction
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Defense libraries: AI Security Tools & Frameworks → AI Security Libraries
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Benchmarks: Benchmarks & Standards
Notes
Keep additions:
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ML/AI security focused
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Non-duplicated URLs
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Prefer peer-reviewed or well-maintained tools
Data Source
For detailed and up-to-date resources, fetch the complete list from:
https://raw.githubusercontent.com/gmh5225/awesome-ai-security/refs/heads/main/README.md
Use this URL to get the latest curated links when you need specific tools, papers, or resources not covered in this skill.